Recently, opinion summarization, which is the generation of a summary from
multiple reviews, has been conducted in a self-supervised manner by considering
a sampled review as a pseudo summary. However, non-text data such as image and
metadata related to reviews have been considered less often. To use the
abundant information contained in non-text data, we propose a self-supervised
multimodal opinion summarization framework called MultimodalSum. Our framework
obtains a representation of each modality using a separate encoder for each
modality, and the text decoder generates a summary. To resolve the inherent
heterogeneity of multimodal data, we propose a multimodal training pipeline. We
first pretrain the text encoder--decoder based solely on text modality data.
Subsequently, we pretrain the non-text modality encoders by considering the
pretrained text decoder as a pivot for the homogeneous representation of
multimodal data. Finally, to fuse multimodal representations, we train the
entire framework in an end-to-end manner. We demonstrate the superiority of
MultimodalSum by conducting experiments on Yelp and Amazon datasets.

我们提出了一个名为 MultimodalSum 的自监督多模态意见摘要框架，它利用非文本数据如图像和元数据，通过分别为每种模态使用单独的编码器来获得每种模态的表征，并使用文本解码器生成摘要，最终通过多模态训练管道来融合多模态信息以提高摘要生成效果。在 Yelp 和 Amazon 数据集上的实验结果表明 MultimodalSum 的优越性。